Systems and methods for filtering of computer vision generated tags using natural language processing

ABSTRACT

This disclosure relates to systems, methods, and computer readable media for performing filtering of computer vision generated tags in a media file for the individual user in a multi-format, multi-protocol communication system. One or more media files may be received at a user client. The one or more media files may be automatically analyzed using computer vision models, and computer vision generated tags may be generated in response to analyzing the media file. The tags may then be filtered using Natural Language Processing (NLP) models, and information obtained during NLP tag filtering may be used to train and/or fine-tune one or more of the computer vision models and the NLP models.

TECHNICAL FIELD

This disclosure relates generally to systems, methods, and computerreadable media for filtering of computer vision generated tags usingnatural language processing and computer vision feedback loops.

BACKGROUND

The proliferation of personal computing devices in recent years,especially mobile personal computing devices, combined with a growth inthe number of widely-used communications formats (e.g., text, voice,video, image) and protocols (e.g., SMTP, IMAP/POP, SMS/MMS, XMPP, etc.)has led to a communication experience that many users find fragmentedand difficult to search for relevant information in thesecommunications. Users desire a system that will discern meaningfulinformation about visual media that is sent and/or received acrossmultiple formats and communication protocols and provide more relevantuniversal search capabilities, with ease and accuracy.

In a multi-protocol system, messages can include shared items thatinclude files or include pointers to files that may have visualproperties. These files can include images and/or videos that lackmeaningful tags or descriptions about the nature of the image or video,causing users to be unable to discover said content in the future viasearch or any means other than direct user lookup (i.e., a userspecifically navigating to a precise file in a directory or anattachment in a message). For example, a user may have received emailmessages with visual media from various sources that are receivedthrough emails in an email system over the user's lifetime. However, dueto the passage of time, the user may be unaware where the particularvisual media (e.g., image/picture and video) may have been stored orarchived. Therefore, the user may have to manually search through thevisual images or videos so as to identify an object, e.g., an animal ora plant that the user remembers viewing in the visual media when it wasinitially received. This can be time consuming, inefficient andfrustrating for the user. In some cases wherein the frequency of visualmedia sharing is high, this process can result in a user not being ableto recall any relevant detail of the message for lookup (such as exacttimeframe, sender, filename, etc.) and therefore “lose” the visualmedia, even though the visual media is still resident in its originalsystem or file location.

Recently, a great deal of progress has been made in large-scale objectrecognition and localization of information in images. Most of thissuccess has been achieved by enabling efficient learning of deep neuralnetworks (DNN), i.e., neural networks with several hidden layers.Although deep learning has been successful in identifying someinformation in images, a human-comparable automatic annotation of imagesand videos (i.e., producing natural language descriptions solely fromvisual data or efficiently combining several classification models) isstill far from being achieved.

In large systems, recognition parameters are not personalized at a userlevel. For example, recognition parameters may not account for userpreferences when searching for content in the future, and can returnvarying outputs based on a likely query type, importance, or objectnaming that is used conventionally (e.g., what a user calls a coffee cupversus what other users may call a tea cup, etc.). Therefore., theconfidence of the output results may change based on the query terms orobject naming.

The subject matter of the present disclosure is directed to overcoming,or at least reducing the effects of, one or more of the problems setforth above. To address these and other issues, techniques that enablefiltering or “de-noising” computer vision-generated tags or annotationsin images arid videos using feedback loops are described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating a server-entry point networkarchitecture infrastructure, according to one or more disclosedembodiments.

FIG. 1B is a block diagram illustrating a client-entry point networkarchitecture infrastructure, according to one or more disclosedembodiments.

FIG. 2A is a block diagram illustrating a computer which could be usedto execute the multi-format, multi-protocol contextualized indexingapproaches described herein according to one or more disclosedembodiments.

FIG. 2B is a block diagram illustrating a processor core, which mayreside on a computer according to one or more disclosed embodiments.

FIG. 3 is a flow diagram illustrating an example of a method forfiltering computer vision generated tags, according to one or moredisclosed embodiments.

FIG. 4 is a diagram for an exemplary image that depicts computergenerated tags in order of confidence level.

FIG. 5 shows an example of a multi-format, multi-protocol, universalsearch results page for a particular query, according to one or moredisclosed embodiments.

DETAILED DESCRIPTION

Disclosed are systems, methods, and computer readable media forextracting meaningful information about the nature of a visual item incomputing devices that have been shared with participants in a networkacross multiple formats and multiple protocol communication systems.More particularly, but not by way of limitation, this disclosure relatesto systems, methods, and computer readable media to permit computingdevices, e.g., smartphones, tablets, laptops, wearable devices, and thelike, to detect and establish meaningful information in visual imagesacross multi-format/multi-protocol data objects that can be stored inone or more centralized servers. Also, the disclosure relates tosystems, methods, and computer-readable media to run visual mediathrough user-personalized computer vision learning services to extractmeaningful information about the nature of the visual item, so as toserve the user more relevant and more universal searching capability.For simplicity and ease of understanding, many examples and embodimentsare discussed with respect to communication data objects of one type(e.g., images). However, unless otherwise noted, the examples andembodiments may apply to other data object types as well (e.g., audio,video data, emails, MMS messages).

As noted above, the proliferation of personal computing devices and dataobject types has led to a searching experience that many users findfragmented and difficult. Users desire a system that will provideinstant and relevant search capabilities whereby the searcher may easilylocate a specific image or video which has been shared with them usingany type of sharing method and which may or may not contain any relevanttext-based identification matching the search query strand such as adescriptive filename, meta data, user-generated tags, etc.

As used herein, computer vision can refer to methods for acquiring,processing, analyzing, and understanding images or videos in order toproduce meaningful information from the images or videos.

In at least one embodiment, a system, method, and computer-readablemedia for filtering Computer Vision (CV) generated tags or annotationson media files is disclosed. The embodiment may include running orimplementing one or more image analyzer (IA) models from an imageanalyzer (IA) server on the media files for generating CV tags. In anembodiment, the models can include object segmentation, objectlocalization, object detection/recognition, natural language processing(NLP), and a relevance feedback loop model for training and filtering.

In another embodiment, the image analyzers (IA) may be sequenced basedon a particular user and the evolving nature of algorithms. For example,the sequencing of IA analyzer models may change as algorithms for actualNLP detection, classification, tagging, etc. evolve. The sequencing ofIA analyzer models may also be changed based on user. For example,knowing that user A typically searches for people and not scenery, theAI sequencing may be adjusted to run additional models for facialrecognition and action detection, while avoiding models for scenedetection.

In another embodiment, the relevance feedback model can include afeedback loop where ‘generic’ tags that are created for objects may beprocessed or filtered with personalized NLP and searches for thefiltered tags in the ‘specific object’ or ‘segmentation’ models, and, ifthere is a match, then the tags' confidence may be increased. This loopmay be repeated until a desired overall confidence threshold is reached.

In another embodiment, an object segmentation model may be run on imagefiles that may have been shared with the user in a multi-protocol,multi-format communication system. The object segmentation model may beconfigured to analyze pictures using one or more algorithms, so as toidentify or determine distinct objects in the picture. In an embodiment,an object localization model may be performed on the image, along witheach of the detected ‘pixel-level masks’ (i.e., the precise area thatthe object covers in the image), to identify locations of distinctobjects in the image. Object localization may be used to determine anapproximation of what the objects are and where the objects are locatedin the image.

In an embodiment, deep object detection may be implemented by using oneor more image corpora together with NLP models to filter CV generatedtags. NLP methods may be used to represent words and contextuallyanalyze tags in text form. An NLP model may allow for a semanticallymeaningful way to filter the tags and identify outliers in the CVgenerated tags.

In another embodiment, a relevance feedback loop may be implemented,whereby the NLP engine may filter, or “de-noise,” the CV generated tagsby detecting conceptual similarities to prioritize similar tags anddeprioritize irrelevant tags. For example, when the system detects aquestionable tag (i.e., confidence level is low), the system may recheckthe tag to ascertain whether discarding the tag is advised. Furthermore,a CV tag-filtering engine based on a training set annotated at thebounding-box level (object's location) may create rules related to thespatial layout of objects and therefore adapt the NLP classifier tofilter related definitions based on these layouts. For example, ineveryday photos/images, the ‘sky’ is usually above the ‘sea’. The systemmay search for pictures from external datasets based on the subject ofthe discarded tag to verify whether removing the outlier was accurate.Results obtained from the search may be used to train NLP and computervision using the images in the image dataset of the subject matter ofthe discarded tag.

In a non-limiting example, a user might want to find a picture or imagethat a certain person (e.g., his friend Bob) sent to him that depicts acertain subject (e.g., Bob and Bob's pet Llama), via a general query.The universal search approach of this disclosure allows a user to searchfor specific items—but in a general way—using natural language,regardless of the format or channel through which the message/file came.So, the user could, for example, search for “the picture Bob sent me ofhim with his Llama” without having to tell the system to search for aJPEG file or the like. The user could also simply search for “Llama” or“‘Bob’ and ‘animal’” to prompt the search system to identify the imagevia it's CV tags (which contain general concepts such as “animal” andspecific concepts such as “Bob” and “Llama”), as opposed to locating theimage via filename, metadata, message body context, or any otherstandard parameter.

As new data/content is on-boarded into the system, the data content canhe categorized and sharded, and insights that can be derived fromanalyzing the data, for example, language patterns, can be used tocreate an overarching user-personality profile containing keyinformation about the user. That key information can be used toinfluence the weights of the various criteria of the index analyzer forthat particular user. The index analyzer for a particular user can beautomatically updated on an ongoing, as-needed, as-appropriate, orperiodic basis, for example. Additionally, a current instance of ananalyzer can be used by a user to perform a search, while another (soonto be more current) instance of the analyzer updates. Thus, for example,the words and expressions that a particular user uses when searching,can become part of a machine learned pattern. If a user on-hoards emailaccounts, an index analyzer will pull historical data from the accountsand analyze that data. One or more analyzers discussed herein cancomprise one or more variations of algorithms running independently orin combination, sequentially, or in parallel.

Referring now to FIG. 1A, a server-entry point network architectureinfrastructure 100 is shown schematically. Infrastructure 100 containscomputer networks 101. Computer networks 101 include many differenttypes of computer networks, such as, but not limited to, the World WideWeb, the Internet, a corporate network, and enterprise network, or aLocal Area Network (LAN). Each of these networks can contain wired orwireless devices and operate using any number of network protocols(e.g., TCP/IP). Networks 101 may be connected to various gateways androuters, connecting various machines to one another, represented, e.g.,by sync server 105, end user computers 103, mobile phones 102, andcomputer servers 106-109. In some embodiments, end user computers 103may not be capable of receiving SMS text messages, whereas mobile phones102 are capable of receiving SMS text messages. Also shown ininfrastructure 100 is a cellular network 103 for use with mobilecommunication devices. Cellular networks support mobile phones and manyother types of devices (e.g., tablet computers not shown). Mobiledevices in the infrastructure 100 are illustrated as mobile phone 102.Sync server 105, in connection with database(s) 104, may serve as thecentral “brains” and data repository, respectively, for themulti-protocol, multi-format communication composition and inbox feedsystem to be described herein. Sync server can comprise an imageanalyzer (IA) server, or be in signal with an external IA server (notshown). In the server-entry point network architecture infrastructure100 of FIG. 1A, centralized sync server 105 may be responsible forquerying and obtaining all the messages from the various communicationsources for individual users of the system and keeping themulti-protocol, multi-format communication inbox feed for a particularuser of the system synchronized with the data on the various third partycommunication servers that the system is in communication with.Database(s) 104 may be used to store local copies of messages sent andreceived by users of the system, data objects of various formats, aswell as individual documents associated with a particular user, whichmay or may not also be associated with particular communications of theusers. Database(s) can be used to store an image dataset organizedaccording to a particular subject matter area and personalizationinformation by a particular user. As such, the database portion allottedto a particular user can contain image information for a particular userthat maps to a global dataset/corpus of images related to a subjectmatter area.

Server 106 in the server-entry point network architecture infrastructure100 of FIG. 1A represents a third party email server (e.g., a GOOGLE® orYAHOO! ® email server). (GOOGLE is a registered service mark of GoogleInc. YAHOO! is a registered service mark of Yahoo! Inc.). Third partyemail server 106 may be periodically pinged by sync server 105 todetermine whether particular users of the multi-protocol, multi-formatcommunication composition and inbox feed system described herein havereceived any new email messages via the particular third-party emailservices. Server 107 represents a represents a third party instantmessage server (e.g., a YAHOO! Messenger or AOL® Instant Messagingserver). (AOL is a registered service mark of AOL Inc.). Third partyinstant messaging server 107 may also be periodically pinged by syncserver 105 to determine whether particular users of the multi-protocol,multi-format communication composition and inbox feed system describedherein have received any new instant messages via the particularthird-party instant messaging services. Similarly, server 108 representsa third party social network server (e.g., a FACEBOOK® or TWITTER®server). (FACEBOOK is a registered trademark of Facebook, Inc.; TWITTERis a registered service mark of Twitter, Inc.). Third party socialnetwork server 108 may also be periodically pinged by sync server 105 todetermine whether particular users of the multi-protocol, multi-formatcommunication composition and inbox feed system described herein havereceived any new social network messages via the particular third-partysocial network services. It is to be understood that, in a “push-based”system, third party servers may push notifications to sync server 105directly, thus eliminating the need for sync server 105 to periodicallyping the third party servers. Finally, server 109 represents a cellularservice provider's server. Such servers may be used to manage thesending and receiving of messages (e.g., email or SMS text messages) tousers of mobile devices on the provider's cellular network. Cellularservice provider servers may also be used: 1) to provide geo-fencing forlocation and movement determination; 2) for data transference; and/or 3)for live telephony (i.e., actually answering and making phone calls witha user's client device). In situations where two ‘on-network’ users arecommunicating with one another via the multi-protocol, multi-formatcommunication system itself, such communications may occur entirely viasync server 105, and third party servers 106-109 may not need to becontacted.

Referring now to FIG. 1B, a client-entry point network architectureinfrastructure 150 is shown schematically. Similar to infrastructure 100shown in FIG. 1A, infrastructure 150 contains computer networks 101.Computer networks 101 may again include many different types of computernetworks available today, such as the Internet, a corporate network, ora Local Area Network (LAN). However, unlike the server-centricinfrastructure 100 shown in FIG. 1A, infrastructure 150 is aclient-centric architecture. Thus, individual client devices, such asend user computers 103 and mobile phones 102 may be used to query thevarious third party computer servers 106-109 to retrieve the variousthird party email, IM, social network, and other messages for the userof the client device. Such a system has the benefit that there may beless delay in receiving messages than in a system where a central serveris responsible for authorizing and pulling communications for many userssimultaneously. Also, a client-entry point system may place less storageand processing responsibilities on the central multi-protocol,multi-format communication composition and inbox feed system's servercomputers since the various tasks may be distributed over a large numberof client devices. Further, a client-entry point system may lend itselfwell to a true, “zero knowledge” privacy enforcement scheme. Ininfrastructure 150, the client devices may also be connected via thenetwork to the central sync server 105 and database 104. For example,central sync server 105 and database 104 may be used by the clientdevices to reduce the amount of storage space needed on-board the clientdevices to store communications-related content and/or to keep all of auser's devices synchronized with the latest communication-relatedinformation and content related to the user. It is to be understoodthat, in a “push-based” system, third party servers may pushnotifications to end user computers 102 and mobile phones 103 directly,thus eliminating the need for these devices to periodically ping thethird party servers.

Referring now to FIG. 2A, an example processing device 200 for use inthe communication systems described herein according to one embodimentis illustrated in block diagram form. Processing device 200 may servein, e.g., a mobile phone 102, end user computer 103, sync server 105, ora server computer 106-109. Example processing device 200 comprises asystem unit 205 which may be optionally connected to an input device 230(e.g., keyboard, mouse, touch screen, etc.) and display 235. A programstorage device (PSI)) 240 (sometimes referred to as a hard disk, flashmemory, or non-transitory computer readable medium) is included with thesystem unit 205. Also included with system unit 205 may be a networkinterface 220 for communication via a network (either cellular orcomputer) with other mobile and/or embedded devices (not shown). Networkinterface 220 may be included within system unit 205 or be external tosystem unit 205. In either case, system unit 205 will be communicativelycoupled to network interface 220. Program storage device 240 representsany form of non-volatile storage including, but not limited to, allforms of optical and magnetic memory, including solid-state storageelements, including removable media, and may be included within systemunit 205 or be external to system unit 205. Program storage device 240may be used for storage of software to control system unit 205, data foruse by the processing device 200, or both.

System unit 205 may be programmed to perform methods in accordance withthis disclosure. System unit 205 comprises one or more processing units,input-output (I/O) bus 225 and memory 215. Access to memory 215 can beaccomplished using the communication bus 225. Processing unit 210 mayinclude any programmable controller device including, for example, amainframe processor, a mobile phone processor, or, as examples, one ormore members of the INTEL® ATOM™, INTEL® XEON™, and INTEL® CORE™processor families from Intel Corporation and the Cortex and ARMprocessor families from ARM. (INTEL, INTEL ATOM, XEON, and CORE aretrademarks of the Intel Corporation. CORTEX is a registered trademark ofthe ARM Limited Corporation. ARM is a registered trademark of the ARMLimited Company). Memory 215 may include one or more memory modules andcomprise random access memory (RAM), read only memory (ROM),programmable read only memory (PROM), programmable read-write memory,and solid-state memory. As also shown in FIG. 2A, system unit 205 mayalso include one or more positional sensors 245, which may comprise anaccelerometer, urometer, global positioning system (GPS) device, or thelike, and which may be used to track the movement of user clientdevices.

Referring now to FIG. 2B, a processing unit core 210 is illustrated infurther detail, according to one embodiment. Processing unit core 210may be the core for any type of processor, such as a micro-processor, anembedded processor, a digital signal processor (DSP), a networkprocessor, or other device to execute code. Although only one processingunit core 210 is illustrated in FIG. 2B, a processing element mayalternatively include more than one of the processing unit core 210illustrated in FIG. 2B. Processing unit core 210 may be asingle-threaded core or, for at least one embodiment, the processingunit core 210 may be multithreaded, in that, it may include more thanone hardware thread context (or “logical processor”) per core.

FIG. 2B also illustrates a memory 215 coupled to the processing unitcore 210. The memory 215 may be any of a wide variety of memories(including various layers of memory hierarchy), as are known orotherwise available to those of skill in the art. The memory 215 mayinclude one or more code instruction(s) 250 to be executed by theprocessing unit core 210. The processing unit core 210 follows a programsequence of instructions indicated by the code 250. Each instructionenters a front end portion 260 and is processed by one or more decoders270. The decoder may generate as its output a micro operation such as afixed width micro operation in a predefined format, or may generateother instructions, microinstructions, or control signals which reflectthe original code instruction. The front end 260 may also includeregister renaming logic 262 and scheduling logic 264, which generallyallocate resources and queue the operation corresponding to the convertinstruction for execution.

The processing unit core 210 is shown including execution logic 280having a set of execution units 285-1 through 285-N. Some embodimentsmay include a number of execution units dedicated to specific functionsor sets of functions. Other embodiments may include only one executionunit or one execution unit that can perform a particular function. Theexecution logic 280 performs the operations specified by codeinstructions.

After completion of execution of the operations specified by the codeinstructions, back end logic 290 retires the instructions of the code250. In one embodiment, the processing unit core 210 allows out of orderexecution but requires in order retirement of instructions. Retirementlogic 295 may take a variety of forms as known to those of skill in theart (e.g., re-order buffers or the like). In this manner, the processingunit core 210 is transformed during execution of the code 250, at leastin terms of the output generated by the decoder, the hardware registersand tables utilized by the register renaming logic 262, and anyregisters (not shown) modified by the execution logic 280.

Although not illustrated in FIG. 2B, a processing element may includeother elements on chip with the processing unit core 210. For example, aprocessing element may include memory control logic along with theprocessing unit core 210. The processing element may include I/O controllogic and/or may include I/O control logic integrated with memorycontrol logic. The processing element may also include one or morecaches.

FIG. 3 illustrates an example dataflow diagram 300 for filteringComputer Vision (CV) generated tags or annotations on media files,according to one or more disclosed embodiments. Data flow diagram 300may include running or implementing one or more image analyzer (IA)models on the media files for generating computer vision tags for auser. In some embodiments., data flow 300 may be implemented onimages/pictures by static recognition of frames, and/or it may beimplemented on videos (e.g., on a per-frame basis for all frames in thevideo, or for select frames in the video based on performing a scenechange detection analysis), e.g., via the performance of spatiotemporaldecomposition of each said frame in the video. In some non-limitingembodiments, the IA models can include object segmentation, objectlocalization, object detection, scene recognition, and other various NLPmethods to aid in the tag “fusion” process. In another embodiment, theIA models may be sequenced based on a particular user and the evolvingnature of algorithms. For example, the sequencing of IA analyzer modelsmay be changed as algorithms for actual NLP detection, classification,tagging, etc. evolve through relevance feedback loops. The sequencing ofIA analyzer models may also be changed based on user preferences. Forexample, knowing that a particular user typically searches for peopleand not scenery, the AI sequencing may be adjusted for that particularuser to run additional models such as facial recognition and actiondetection while avoiding models for scene detection.

Data flow 300 starts at 302 where messaging content may be received andimported into a multi-protocol, multi-format communication system on auser client device (or user-client). For example, messaging content maybe received as messages and/or other shared items that can include mediafiles or point to media files within the message. Media files mayinclude visual properties such as, for example, pictures or videos thatmay be included in the messaging content. In an embodiment, themessaging content including the media files (for example,pictures/images or videos) may be displayed to the user as messagingcontent in a user interface at a client application.

Next, one or more image analyzer (IA) models may be automatically run onthe images and videos to determine computer vision tags or annotationsfor one or more distinct objects in the images (in 304) or videos (in306). Media files that are received may be separated into images andvideos, and one or more IA models may be run on the images and videosbased on the format of the media files.

As shown in FIG. 3, messaging content that is received as video (in 306)may be decomposed by extracting all sequential frames or a discretesample of frames or groups of frames based on a scene detectionalgorithm in 340. Next, in 342, tags may be identified and collectedfrom output of filtered image tags (in 334). Next, in 344, aspatiotemporal fusion model may be run. The spatiotemporal fusion modelmay combine insights obtained from each frame such as, for example, thetags obtained in 342 may be filtered based on spatial and temporalconstraints. The filtered tags along with the accompanying timestampsmay be collected to form a semantically meaningful representation of thevideo sequence.

Also shown in FIG. 3, messaging content that is received as images maybe analyzed using one or more AI models. The one or more AI models maybe performed in parallel or serially. FIG. 3 illustrates a parallelscheme of implementing the one or more AI models on images.

Object detection may be run on the image in 308. In an embodiment,object detection may be implemented as one or more object detectionmodels to determine generic classes of objects. The object detectionmodel analyzes the image to determine tags for generic categories ofitems in the image such as, for example, determining tags at differentabstraction levels such as person, automobile, plant, animal or thelike, but also dog, domestic doc, Labrador dog. Inter-model fusion maybe performed in 316, whereby tags obtained from running several objectdetection models on the image may be combined to generate tags in 324defining labels for each detected object.

Object localization may be run on the image in 310. In an embodiment,object localization may be implemented as one or more objectlocalization models. For example, one or more object localization modelsmay be performed on the image to identify locations of distinct objectsin the image. Object localization may be used to determine anapproximation of what the objects are (i.e., labels) and where theobjects are located (i.e., object window defining pixel coordinates (x,y, width, height) on the image. Inter-model fusion may be performed in318 whereby tags obtained from running several object detection modelson the image may be combined to generate tags in 326 defining labels andboundaries for each detected object.

Object segmentation may be run on the image in 312. Object segmentationmay be implemented as one or more object segmentation models. In anembodiment, an object segmentation model may analyze the image toidentify or determine distinct objects in the image (i.e., labels) andsegmentation mask/object outline of the object (i.e., pixels identifiedto a cluster in which they belong) such as, for example, ‘animal’ andits mask or ‘table’ and its mask. In an example of a picture/image of aconference room having chairs and a conference table, objectsegmentation may be performed to segment the image by identifying one ormore objects in the picture such as, for example, identification ofthree objects where each object may be one of the chairs in the image.In an embodiment, one or more additional object segmentation models maybe applied to recognize faces and humans in the image. Objectsegmentation may generate a segmentation map that may be used to filtertags obtained in other IA models. Inter-model fusion may be performed in320, whereby tags obtained from running several object segmentationmodels on the image may be combined to generate tags in 328 that definelabels and segmentation mask/object outline for each detected object.

Scene/place recognition may be performed on the image in 314. In anembodiment, scene/place recognition may be implemented as one or morescene/place recognition modes that may be trained to recognize thescenery depicted in the image, for example, scenery defining outdoors,indoors, sea or ocean, seashore, beach, or the like. Model fusion may beperformed in 322, whereby tags obtained from running several scenerecognition models on the image may be combined to generate tags in 330that define scenes in the image. For example, the scene/placerecognition model may be used to enrich the set of tags obtained frommodels 308, 310, 312 and drive the filtering of tags in 308, 310, 312 byfiltering out conceptual mismatches to determine whether an objectdetected in another model 308, 310, 312 may be found at the location inthe image, for example, a dog cannot be detected at a location where skyis identified in the image.

In an embodiment, deep detection may use a deep neural network (DNN)that may produce meaningful tags that provide a higher precision ofdetection after proper training on a large set of images belonging toall desired categories. For training the DNN, one may use one or moresets of annotated images (generally referred to as a dataset or corpus)as a baseline. An image dataset/corpus may be a set of annotated imageswith known relational information that have been manually tagged andcurated. In one example, a baseline image dataset/corpus that may beused can be a subset of the image-net dataset (which is available athttp://www.image-net.org/). In an example, the image dataset/corpus maybe augmented by web crawling other image sources and combining theseimage sources into the baseline dataset/corpus for training the imagedataset/corpus. In another embodiment, an image dataset/corpus may betrained by using textual information that may be received in a messagethat has been shared with the user. For example, textual informationreceived in a message, either in the body or subject line such as, forexample, “an image of a plane” may be used to identify tags orannotations that may be used for content in the image.

In an embodiment, after generic classification (in 308), or localization(in 310), or segmentation (in 312), or scene detection (in 314), theimage in 304 may be further analyzed through a specific model based onone or more categories that were identified in the image. For example,if one of the pieces of the image was classified as belonging to a plantcategory, the image may be analyzed through a specific plantdataset/corpus for identifying the specific type of plant using theplant dataset/corpus. Alternatively, if the image was classified as aglass category, the image may be classified as a specific utensil suchas, for example, classified as a cup. These insights may be gathered forthe entire image using models that may be implemented based on thecategory that were identified for the objects in the image.Particularly, the system may gather insights (i.e., identification oftags for the image) during implementing one or more of the specificmodels on the pieces of the image and store these tags in memory. In anembodiment, results that are obtained from implementing one or moremodels may be ranked based on a confidence level.

Next, in 332, after generic classification (in 308), localization (in310), segmentation (in 312) or scene detection (in 314), intra-modelfusion may be performed on the outputs of tags determined in steps 324,326, 328, and 330. In an embodiment, the system may combine tagsobtained from each model (in 324, 326, 328, and 330) (to combine theinsights from the several models for, in embodiments and determine tagsof different nature. For example, the results from combining insightsare concatenated. Information that is concatenated is used to break upthe image intelligently so that each object does not include portions ofother objects (i.e., an object contour does not include portions ofother objects in the image). For example, in an image with a person anda car, the image may be intelligently broken up so that the face of theperson is distinct from portions associated with the car so that thesystem can identify objects in the image, how big the objects are inrelation to other objects in the image and their location in the image.The output of intra-model fusion may produce tags for objects and theirconfidence values for the object tags in the image.

In an embodiment, in intra-level fusion (in 332), the system may weightimportance of the objects in the image using a depth model. The depthmodel may determine depth or focus in the image in order to perceive ifthe objects identified in the image may be further back or closer infront. For example, based on a determination that an object identifiedis further hack, a rule may be implemented that rates the object as lessimportant. Similarly, another rule may weight an object more importantif it has less depth. An index of weights for the image may bedetermined based on the depth model that may be implemented on theimage.

Next, in 334, a Natural Language Processing (NLP) model may beimplemented to filter the tags that are generated in intra-model fusion(in 332). In some embodiments, tag filtering can include inter-level andintra-level tag filtering. Filtering may be used to filter theautomatically generated tags by selecting tags having the highestconfidence values and/or selecting tags that are conceptually closer.

Inter-Level Tag Filtering

Object detection models may be of similar nature or not, i.e. trained todetect a large variety of objects (e.g. hundreds of object classes)hereby called ‘generic,’ or trained to detect specific objects (e.g.tens of classes or even of single class such as human faces,pedestrians, etc.) hereby called ‘specific.’

Running object detection models of similar nature, i.e., of only‘generic’ or only ‘specific’, may produce competing lists of tags withthe same or similar properties that may also containing differentassessed confidence values. Inter-level tag filtering may use confidencere-ranking and NLP-base methods to filter and prioritize those tags by,for example, 1) selecting the tags that are conceptually closer; and 2)accumulating the confidence of those tags and selecting the mostconfident ones. For example, as shown in FIG. 4, running one or moreobject detection models may produce one or more listsautomatically-extracted annotations or tags for the image of a personholding a microphone. By filtering and/or sorting the tags as before,such a system may intelligently select the 5 tags with the highestassessed confidence values, i.e. ‘gasmask’—45%, ‘microphone’—22%, lenscap—15%, barbell—10%, dumbbell—8%. NLP may be applied in order to inferthe “natural” meanings of those tags and therefore detect an “outlier”,i.e. the tag that is conceptually less similar to the rest. For theillustrated example in FIG. 4, using a NLP classifier, the outlier couldbe a ‘gasmask’.

Intra-Level Tag Filtering

Running object detection models of different nature, i.e., of ‘generic’and ‘specific’ nature, may produce competing or complementary lists oftags and confidence values, e.g. tags such as ‘Labrador Retriever’, ‘gundog’, ‘dog’, ‘domestic dog’, ‘Canis lupus familiaris’, ‘animal’, ‘cat’,‘street’). Intra-level filtering based on NLP methods may produce anatural hierarchy of those tags by removing the outliers (‘cat’,‘street’) as in the inter-level filtering case and by also creating anabstract-to-less-abstract hierarchy (‘animal’, ‘dog’, ‘domestic dog’,‘gun dog’, ‘Labrador Retriever’, ‘Canis lupus familiaris’).

Using NLP methods to represent words and contextually analyze text, theNLP model may learn to map each discrete word in a given vocabulary(e.g., a Wikipedia corpus) into a low-dimensional continuous vectorspace based on simple frequencies of occurrence. This low-dimensionalrepresentation may allow for a geometrically meaningful way of measuringdistance between words, which are treated as points in a mathematicallytractable manifold. Consequently, the top-5 tags of FIG. 4 may bere-ranked based on their pairwise distance in the new manifold andtherefore make possible outliers stand out because of a large distancevalue. In the example of FIG. 4, gasmask may be conceptually dissimilarto other tags in the list.

In an embodiment, a relevance feedback loop may be implemented wherebythe NLP engine may “de-noise” the CV generated tags by detectingconceptual similarities to prioritize similar tags and de-prioritizeirrelevant tags. For example, when the system detects a questionable tag(i.e., confidence level is low), the system may recheck the tag toascertain whether discarding the tag is advised. Furthermore, the CV tagengine based on a training set annotated at the bounding-box level(object's location) may create rules related to the spatial layout ofobjects and therefore adapt the NLP classifier to filter relateddefinitions based on these layouts. For example, in everydayphotos/images, the ‘sky’ is—usually—above the ‘sea’. The system maysearch for pictures from external datasets based on the subject of thediscarded tag to verify whether removing the outlier was accurate.Results obtained from the search may be used to train NLP and computervision using the images in the image dataset of the subject matter ofthe discarded tag.”

Referring now to FIG. 5, an example of a multi-format, multi-protocolcommunication universal search results page 560 for a particular queryis shown, according to one or more disclosed embodiments. At the top ofpage 560 may be a search input box 561. A user may enter his or herdesired query string into the search input box 561 and then click on themagnifying glass icon to initiate the search process. Search results row562 may be used for providing the user with a choice of additionalsearch-related features. For example, the user may be provided with aselection between a “global” search, i.e., searching everywhere in theapplication's ecosystem, and a “narrow” search, i.e., searching onlythrough content on a screen or small collection of screens. As shown inFIG. 5, search results 563 may be displayed in a unified feed or can begrouped by type (e.g., messages, files, etc.), query type, search areaselection (e.g., “global” v. “narrow”), or time. Each search result mayoptionally include an indication of the messages format 565 and/or atime stamp 564 to provide additional information to the user. A givenimplementation may also optionally employ an “Other Results” feed 566 asa part of the same user interface that displays the search results 563.Such other results could include, for example, information pertaining toa user's contacts, such as an indication that a user was a source of aparticular message or group of messages, or that a particular user wasthe source of particular documents. These results could come fromsources other than traditional message-related sources, and exist inother formats, e.g., a user's personal file collection stored in acentralized database, data object of various formats (e.g., personalprofile information from contacts of the user, images files, videofiles, audio files, and any other file/data object that can be indexedas disclosed herein). Search results could also include tagscorresponding to portions of visual files/visual data objects. Asdiscussed in detail above, such tags may be generated by an imageanalyzer system, which analyzes pictures and/or videos. The possiblesources and results identified are included by way of illustration, notlimitation.

EXAMPLES

The following examples pertain to further embodiments.

Example 1 is a non-transitory computer readable medium comprisingcomputer readable instructions, which, upon execution by one or moreprocessing units, cause the one or more processing units to: receive amedia file for a user, wherein the media file includes one or moreobjects; automatically analyze the media file using computer visionmodels responsive to receiving the media file; generate tags for theimage responsive to automatically analyzing the media file; filter thetags using Natural Language Processing (NLP) models; and utilizeinformation obtained during filtering of the tags to fine-tune one ormore of the computer vision models and the NLP models, wherein the mediafile includes one of an image or a video.

Example 2 includes the subject matter of Example 1, wherein theinstructions to filter the tags using NLP models further compriseinstructions that when executed cause the one or more processing unitsto select tags that are conceptually closer.

Example 3 includes the subject matter of Example 1, wherein theinstructions to train each of the computer vision models and the NLPmodels further comprise instructions that when executed cause the one ormore processing units to recheck outlier tags in an image corpus foraccuracy of the outlier tag.

Example 4 includes the subject matter of Example 1, wherein theinstructions to automatically analyze the media file further compriseinstructions that when executed cause the one or more processing unitsto automatically analyze the media file using one or more of an objectsegmentation model, object localization model or object detection model.

Example 5 includes the subject matter of Example 1, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to analyze the media file using an objectsegmentation model for identifying the extent of distinct objects in theimage.

Example 6 includes the subject matter of Example 1, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to implement an object detection andrecognition model and an object localization model in parallel.

Example 7 includes the subject matter of Example 6, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to implement the object detection andrecognition model to determine tags related to general categories ofitems in the image.

Example 8 includes the subject matter of Example 1, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to implement the object localization modelto identify the location of distinct objects in the image.

Example 9 is a system, comprising: a memory; and one or more processingunits, communicatively coupled to the memory, wherein the memory storesinstructions to cause the one or more processing units to: receive animage for a user, wherein the image includes one or more objects;automatically analyze the image using computer vision models responsiveto receiving the media file; generate tags for the image responsive toautomatically analyzing the image; filter the tags using NaturalLanguage Processing (NLP) models; and utilize information obtainedduring filtering of the tags to fine-tune one or more of the computervision models and the NLP models, wherein the media file includes one ofan image or a video.

Example 10 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units to selecttags that are conceptually closer responsive to filtering the tags usingNLP models.

Example 11 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units torecheck outlier tags in an image corpus for accuracy of the outlier tag.

Example 12 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units toautomatically analyze the image using one or more of an objectsegmentation model, object localization model or object detection model.

Example 13 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units toanalyze the media file using an object segmentation model foridentifying the extent of distinct objects in the image.

Example 14 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units toimplement an object detection model and an object localization model inparallel.

Example 15 includes the subject matter of Example 14, the memory furtherstoring instructions to cause the one or more processing units toimplement the object detection model to determine tags related togeneral categories of items in the image.

Example 16 includes the subject matter of Example 9, the memory furtherstoring instructions to cause the one or more processing units toimplement the object localization model for identifying the location ofdistinct objects in the image.

Example 17 is a computer-implemented method, comprising: receiving animage for a user, wherein the image includes one or more objects;automatically analyzing the image using computer vision modelsresponsive to receiving the media file; generating tags for the imageresponsive to automatically analyzing the image; filtering the tagsusing Natural Language Processing (NLP) models; and utilizinginformation obtained during filtering of the tags to fine-tune one ormore of the computer vision models and the NLP models.

Example 18 includes the subject matter of Example 17, further comprisingselecting tags that are conceptually closer responsive to filtering thetags.

Example 19 includes the subject matter of Example 17, further comprisingrechecking outlier tags in an image corpus for accuracy of the outliertags.

Example 20 includes the subject matter of Example 17, further comprisingautomatically analyzing the image using one or more of an objectsegmentation model, object localization model or object detection model.

Example 21 includes the subject matter of Example 17, further comprisinganalyzing the media file using an object segmentation model foridentifying the extent of distinct objects in the image.

Example 22 includes the subject matter of Example 17, further comprisingimplementing an object detection model and an object localization modelin parallel.

Example 23 includes the subject matter of Example 22, further comprisingimplementing the object detection model to determine tags related togeneral categories of items in the image.

Example 24 includes the subject matter of Example 17, further comprisingimplementing the object localization model to identify a location ofdistinct objects in the image.

Example 25 includes the subject matter of Example 24, further comprisingsearching for visually similar objects in a dataset.

Example 26 includes the subject matter of Example 21, further comprisingsearching for visually similar objects in a dataset.

In the foregoing description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the disclosed embodiments. It will be apparent,however, to one skilled in the art that the disclosed embodiments may bepracticed without these specific details. In other instances, structureand devices are shown in block diagram form in order to avoid obscuringthe disclosed embodiments. References to numbers without subscripts orsuffixes are understood to reference all instance of subscripts andsuffixes corresponding to the referenced number. Moreover, the languageused in this disclosure has been principally selected for readabilityand instructional purposes, and may not have been selected to delineateor circumscribe the inventive subject matter, resort to the claims beingnecessary to determine such inventive subject matter. Reference in thespecification to “one embodiment” or to “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiments is included in at least one disclosed embodiment,and multiple references to “one embodiment” or “an embodiment” shouldnot be understood as necessarily all referring to the same embodiment.

It is also to be understood that the above description is intended to beillustrative, and not restrictive. For example, above-describedembodiments may be used in combination with each other and illustrativeprocess steps may be performed in an order different than shown. Manyother embodiments will be apparent to those of skill in the art uponreviewing the above description. The scope of the invention thereforeshould be determined with reference to the appended claims, along withthe full scope of equivalents to which such claims are entitled.

What is claimed is:
 1. A non-transitory computer readable mediumcomprising computer readable instructions, which, upon execution by oneor more processing units, cause the one or more processing units to:receive a media file for a user, wherein the media file includes one ormore objects; automatically analyze the media file using computer visionmodels responsive to receiving the media file; generate tags for theimage responsive to automatically analyzing the media file; filter thetags using Natural Language Processing (NLP) models; and utilizeinformation obtained during filtering of the tags to fine-tune one ormore of the computer vision models and the NLP models, wherein the mediafile includes one of an image or a video.
 2. The non-transitory computerreadable medium of claim 1, wherein the instructions to filter the tagsusing NLP models further comprise instructions that when executed causethe one or more processing units to select tags that are conceptuallycloser.
 3. The non-transitory computer readable medium of claim 1,wherein the instructions to train each of the computer vision models andthe NLP models further comprise instructions that when executed causethe one or more processing units to recheck outlier tags in an imagecorpus for accuracy of the outlier tag.
 4. The non-transitory computerreadable medium of claim 1, wherein the instructions to automaticallyanalyze the media file further comprise instructions that when executedcause the one or more processing units to automatically analyze themedia file using one or more of an object segmentation model, objectlocalization model or object detection model.
 5. The non-transitorycomputer readable medium of claim 1, wherein the instructions furthercomprise instructions that when executed cause the one or moreprocessing units to analyze the media file using an object segmentationmodel for identifying the extent of distinct objects in the image. 6.The non-transitory computer readable medium of claim 1, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to implement an object detection andrecognition model and an object localization model in parallel.
 7. Thenon-transitory computer readable medium of claim 6, wherein theinstructions further comprise instructions that when executed cause theone or more processing units to implement the object detection andrecognition model to determine tags related to general categories ofitems in the image.
 8. The non-transitory computer readable medium ofclaim 1, wherein the instructions further comprise instructions thatwhen executed cause the one or more processing units to implement theobject localization model to identify the location of distinct objectsin the image.
 9. A system, comprising: a memory; and one or moreprocessing units, communicatively coupled to the memory, wherein thememory stores instructions to cause the one or more processing units to:receive an image for a user, wherein the image includes one or moreobjects; automatically analyze the image using computer vision modelsresponsive to receiving the media file; generate tags for the imageresponsive to automatically analyzing the image; filter the tags usingNatural Language Processing (NLP) models; and utilize informationobtained during filtering of the tags to fine-tune one or more of thecomputer vision models and the NLP models, wherein the media fileincludes one of an image or a video.
 10. The system of claim 9, thememory further storing instructions to cause the one or more processingunits to select tags that are conceptually closer responsive tofiltering the tags using NLP models.
 11. The system of claim 9, thememory further storing instructions to cause the one or more processingunits to recheck outlier tags in an image corpus for accuracy of theoutlier tag.
 12. The system of claim 9, the memory further storinginstructions to cause the one or more processing units to automaticallyanalyze the image using one or more of an object segmentation model,object localization model or object detection model.
 13. The system ofclaim 9, the memory further storing instructions to cause the one ormore processing units to analyze the media file using an objectsegmentation model for identifying the extent of distinct objects in theimage.
 14. The system of claim 9, the memory further storinginstructions to cause the one or more processing units to implement anobject detection model and an object localization model in parallel. 15.The system of claim 14, the memory further storing instructions to causethe one or more processing units to implement the object detection modelto determine tags related to general categories of items in the image.16. The system of claim 9, the memory further storing instructions tocause the one or more processing units to implement the objectlocalization model for identifying the location of distinct objects inthe image.
 17. A computer-implemented method, comprising: receiving animage for a user, wherein the image includes one or more objects;automatically analyzing the image using computer vision modelsresponsive to receiving the media file; generating tags for the imageresponsive to automatically analyzing the image; filtering the tagsusing Natural Language Processing (NLP) models; and utilizinginformation obtained during filtering of the tags to fine-tune one ormore of the computer vision models and the NLP models.
 18. The method ofclaim 17, further comprising selecting tags that are conceptually closerresponsive to filtering the tags.
 19. The method of claim 17, furthercomprising rechecking outlier tags in an image corpus for accuracy ofthe outlier tags.
 20. The method of claim 17, further comprisingautomatically analyzing the image using one or more of an objectsegmentation model, object localization model or object detection model.21. The method of claim 17, further comprising analyzing the media fileusing an object segmentation model for identifying the extent ofdistinct objects in the image.
 22. The method of claim 17, furthercomprising implementing an object detection model and an objectlocalization model in parallel.
 23. The method of claim 22, furthercomprising implementing the object detection model to determine tagsrelated to general categories of items in the image.
 24. The method ofclaim 17, further comprising implementing the object localization modelto identify a location of distinct objects in the image.
 25. The methodof claim 24, further comprising searching for visually similar objectsin a dataset.
 26. The method of claim 21, further comprising searchingfor visually similar objects in a dataset.